ANALYSIS OF REAL-TIME OBJECT DETECTION METHODS FOR ANDROID SMARTPHONE
This paper presents the analysis of real-time object detection method for embedded system, especially the Android smartphone. As we all know, object detection algorithm is a complicated algorithm that consumes high performance hardware to execute the algorithm in real time. However due to the d...
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| Main Author: | |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/4100/1/ICEI.pdf http://eprints.utem.edu.my/id/eprint/4100/ |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | This paper presents the analysis of real-time
object detection method for embedded system, especially the
Android smartphone. As we all know, object detection
algorithm is a complicated algorithm that consumes high
performance hardware to execute the algorithm in real
time. However due to the development of embedded
hardware and object detection algorithm, current
embedded device may be able to execute the object detection
algorithm in real-time. In this study, we analyze the best
object detection algorithm with respect to efficiency, quality
and robustness of the object detection. A lot of object
detection algorithms have been compared such as Scale
Invariant Feature Transform (SIFT), Speeded-Up Feature
Transform (SuRF), Center Surrounded Extrema
(CenSurE), Good Features To Track (GFTT), Maximally-
Stable Extremal Region Extractor (MSER), Oriented
Binary Robust Independent Elementary Features (ORB),
and Features from Accelerated Segment Test (FAST) on the
GalaxyS Android smartphone. The results show that FAST
algorithm has the best combination of speed and object
detection performance. |
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